Processing of Electrophysiological Signals

1. A method comprising: receiving a photoplethysmography (PPG) signal collected by a PPG sensing apparatus; detecting peaks and valleys in the PPG signal; segmenting the PPG signal to provide a first time series of PPG waveforms located between two subsequent valleys in the PPG signal; applying pattern recognition to the first time series with respect to a reference PPG waveform pattern produced based on a mathematical model of the PPG signal by assigning a recognition score to the waveforms in the first time series, wherein the reference PPG waveform pattern is produced with a reaction-diffusion model, with diastolic and systolic phases of a heart coupled with the reaction and diffusion properties of the reaction-diffusion model, and wherein applying the pattern recognition comprises applying cross-correlation analysis between the waveforms in the first time series and the reference PPG waveform pattern; retaining the waveforms in the first time series having a recognition score higher or equal to a recognition threshold; and discarding the waveforms in the first time series having a recognition score lower than the recognition threshold thereby producing a resulting PPG signal.

2. The method of claim 1 , further comprising bandpass filtering the PPG signal.

3. The method of claim 2 , wherein bandpass filtering the PPG signal comprises a joint low-pass and high-pass filtering.

4. The method of claim 1 , wherein detecting the peaks and valleys comprises calculating first and second derivatives of the PPG signal.

5. The method of claim 1 , further comprising normalizing the PPG signal to a unitary range prior to the segmenting.

6. The method of claim 1 , further comprising producing the reference PPG waveform pattern via a self-adaptive nonlinear oscillator.

7. The method of claim 6 , wherein the reference PPG waveform pattern is produced with a neural network.

8. The method of claim 1 , further comprising resealing the reference PPG waveform pattern over time to facilitate applying pattern recognition to time-comparable waveforms.

9. The method of claim 1 , wherein the recognition score includes a cross-correlation index.

10. The method of claim 1 , wherein receiving the PPG signal further includes receiving a second time series of electrocardiography (ECG) signal waveforms, the method further comprising: calculating a first derivative of the resulting PPG signal; performing cross-correlation of the ECG signal waveforms and the first derivative of the resulting PPG signal by assigning to the ECG signal waveforms cross-correlation scores with the first derivative of the resulting PPG signal; comparing with a validation threshold the cross-correlation scores of the ECG signal waveforms; and validating as valid ECG signal waveforms the ECG signal waveforms having cross-correlation scores higher or equal to the validation threshold.

11. The method of claim 10 , further comprising: performing cross-correlation of the ECG signal waveforms and an ECG reference waveform by assigning, to the ECG signal waveforms, second cross-correlation scores with the ECG reference waveform; comparing with a second validation threshold the second cross-correlation scores of the ECG signal waveforms; and validating as valid ECG signal waveforms the ECG signal waveforms having both the cross-correlation scores higher or equal to the validation threshold and the second cross-correlation scores higher or equal to the second validation threshold.

12. The method of claim 10 , further comprising bandpass filtering the ECG signal waveforms.

13. The method of claim 1 , wherein the PPG sensing apparatus comprises a plurality of probes and a front-end device and wherein receiving the PPG signals comprises performing measurements using the probes and receiving measurement information from the front-end device.

14. A method comprising: receiving a photoplethysmography (PPG) signal collected by a PPG sensing apparatus; detecting peaks and valleys in the PPG signal; segmenting the PPG signal to provide a first time series of PPG waveforms located between two subsequent valleys in the PPG signal; producing a reference PPG waveform pattern based on a mathematical model of the PPG signal, the reference PPG waveform pattern being produced with a reaction-diffusion model, with diastolic and systolic phases of a heart coupled with the reaction and diffusion properties of the reaction-diffusion model; applying pattern recognition to the first time series with respect to the reference PPG waveform pattern by assigning a recognition score to the waveforms in the first time series, wherein applying the pattern recognition comprises applying cross-correlation analysis between the waveforms in the first time series and the reference PPG waveform pattern; and based on the pattern recognition, producing a resulting PPG signal.

15. The method of claim 14 , further comprising bandpass filtering the PPG signal.

16. The method of claim 15 , wherein bandpass filtering the PPG signal comprises a joint low-pass and high-pass filtering.

17. The method of claim 14 , wherein detecting the peaks and valleys comprises calculating first and second derivatives of the PPG signal.

18. The method of claim 14 , further comprising normalizing the PPG signal to a unitary range prior to the segmenting.

19. The method of claim 14 , wherein the reference PPG waveform pattern is produced via a self-adaptive nonlinear oscillator.

20. The method of claim 14 , wherein the reference PPG waveform pattern is produced with a neural network.

21. The method of claim 14 , further comprising resealing the reference PPG waveform pattern over time to facilitate applying pattern recognition to time-comparable waveforms.

22. A method comprising: receiving a photoplethysmography (PPG) signal collected by a PPG sensing apparatus; detecting peaks and valleys in the PPG signal; segmenting the PPG signal to provide a first time series of PPG waveforms located between two subsequent valleys in the PPG signal; producing a reference PPG waveform pattern based on a mathematical model of the PPG signal, the reference PPG waveform pattern being produced with a reaction-diffusion model, with diastolic and systolic phases of a heart coupled with the reaction and diffusion properties of the reaction-diffusion model; applying pattern recognition to the first time series with respect to the reference PPG waveform pattern by assigning a recognition score to the waveforms in the first time series, wherein applying the pattern recognition comprises applying cross-correlation analysis between the waveforms in the first time series and the reference PPG waveform pattern, and wherein the recognition score includes a cross-correlation index; and based on the pattern recognition, producing a resulting PPG signal.